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Improving prediction of severe weather

Fast moving severe weather is extremely challenging to predict. The cause of most severe weather and flooding events are mesoscale convective systems (MCS), small-scaled organized cells of intense rainfall. Over the United States, MCS account for over 50% of warm-season precipitation in the Great Plains and over 40% of cold-season precipitation in the southeast.

Ardeshir Ebtahaj researches MCS, and he is working to improve prediction of extreme weather events.

One of his latest projects received funding from NASA’s Global Precipitation Measurement Mission (GPM, https://gpm.nasa.gov/) science team in the amount of $450,000.

NASA’s Global Precipitation Measurement Mission (GPM) is an international mission that sets the standard for spaceborne precipitation measurements. GPM uses satellites to measure Earth's rain and snowfall.

Ebtahaj is collaborating with Liaofan Lin, a research scientist at the Cooperative Institute for Research in the Atmosphere at Colorado State University, and at the National Oceanic and Atmospheric Administration. The researchers are developing a new data assimilation framework to integrate NASA’s high quality satellite observations and frequent (every 30 minutes) estimates of Earth’s rain and snowfall with existing weather models to reduce uncertainty and better predict the MSCs that are responsible for so much severe weather. 

The outcomes of this research will support weather-related decisions on regional to global scales with respect to the impacts of extreme precipitation.

The research will characterize the extent to which advances in machine learning algorithms (specifically, deep learning) can extract information about cloud microphysics and short-term evolution of moist processes that lead to intense rainfall. 

The research will lead to a fundamentally new paradigm for precipitation data assimilation, and will extend applicability of satellite data for improved regional weather prediction.

The research will lead to a short-term (0-36 hours) global forecast product that improves on the quality of existing forecast systems.

With the rising occurrence of severe weather, improved prediction could influence tactical, transportation, public health, and economic decisions and could help protect a wide range of socio-economic sectors.

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